An enterprise-grade RAG system that allows organizations to query their internal documentation using natural language. The system uses vector embeddings, semantic search, and LLM integration to provide accurate, context-aware answers from company knowledge bases. It supports multiple document types, maintains conversation context, and provides source citations for transparency. The system is optimized for latency and cost while maintaining high accuracy.
Employees spent significant time searching through documentation to find answers. Traditional keyword search was insufficient for complex queries.
Document ingestion pipeline processes and chunks documents, generating embeddings stored in Azure Cognitive Search. Query pipeline retrieves relevant chunks, assembles context, and sends to LLM for generation. Redis caching reduces latency and costs.
Optimizing latency and cost for production use while maintaining high accuracy and handling large document collections
Implemented caching, prompt optimization, selective retrieval strategies, and hybrid search combining vector and keyword search
Reduced support ticket volume by 40%, improved employee productivity, and enabled self-service information access